The impact of artificial intelligence in medicine on the future role of the physician

Artificial intelligence in pediatric allergy research

Atopic dermatitis, food allergy, allergic rhinitis, and asthma are among the most common diseases in childhood. They are heterogeneous diseases, can co-exist in their development, and manifest complex associations with other disorders and environmental and hereditary factors. Elucidating these intricacies by identifying clinically distinguishable groups and actionable risk factors will allow for better understanding of the diseases, which will enhance clinical management and benefit society and affected individuals and families. Artificial intelligence (AI) is a promising tool in this context, enabling discovery of meaningful patterns in complex data. Numerous studies within pediatric allergy have and continue to use AI, primarily to characterize disease endotypes/phenotypes and to develop models to predict future disease outcomes. However, most implementations have used relatively simplistic data from one source, such as questionnaires. In addition, methodological approaches and reporting are lacking. This review provides a practical hands-on guide for conducting AI-based studies in pediatric allergy, including (1) an introduction to essential AI concepts and techniques, (2) a blueprint for structuring analysis pipelines (from selection of variables to interpretation of results), and (3) an overview of common pitfalls and remedies. Furthermore, the state-of-the art in the implementation of AI in pediatric allergy research, as well as implications and future perspectives are discussed.

CONCLUSION

AI-based solutions will undoubtedly transform pediatric allergy research, as showcased by promising findings and innovative technical solutions, but to fully harness the potential, methodologically robust implementation of more advanced techniques on richer data will be needed.

WHAT IS KNOWN

• Pediatric allergies are heterogeneous and common, inflicting substantial morbidity and societal costs. • The field of artificial intelligence is undergoing rapid development, with increasing implementation in various fields of medicine and research.

WHAT IS NEW

• Promising applications of AI in pediatric allergy have been reported, but implementation largely lags behind other fields, particularly in regard to use of advanced algorithms and non-tabular data. Furthermore, lacking reporting on computational approaches hampers evidence synthesis and critical appraisal. • Multi-center collaborations with multi-omics and rich unstructured data as well as utilization of deep learning algorithms are lacking and will likely provide the most impactful discoveries.

Deep learning models can predict violence and threats against healthcare providers using clinical notes

Violence, verbal abuse, threats, and sexual harassment of healthcare providers by patients is a major challenge for healthcare organizations around the world, contributing to staff turnover, distress, absenteeism, reduced job satisfaction, and worsening mental and physical health. To enable interventions prior to possible violent episodes, we trained two deep learning models to predict violence against healthcare workers 3 days prior to violent events for case and control patients. The first model is a document classification model using clinical notes, and the second is a baseline regression model using largely structured data. Our document classification model achieved an F1 score of 0.75 while our model using structured data achieved an F1 of 0.72, both exceeding the predictive performance of a psychiatry team who reviewed the same documents (0.5 F1). To aid in the explainability and understanding of risk factors for violent events, we additionally trained a named entity recognition classifier on annotations of the same corpus, which achieved an overall F1 of 0.7. This study demonstrates the first deep learning model capable of predicting violent events within healthcare settings using clinical notes, surpassing the first published baseline of human experts. We anticipate our methods can be generalized and extended to enable intervention at other hospital systems.

A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings

Background and Aims

The rapid expansion of artificial intelligence (AI) within worldwide healthcare systems is occurring at a significant rate. In this context, the Middle East has demonstrated distinctive characteristics in the application of AI within the healthcare sector, particularly shaped by regional policies. This study examined the outcomes resulting from the utilization of AI within healthcare systems in the Middle East.

Methods

A systematic review was conducted across several databases, including PubMed, Scopus, ProQuest, and the Cochrane Database of Systematic Reviews in 2024. The quality assessment of the included studies was conducted using the Authority, Accuracy, Coverage, Objectivity, Date, Significance checklist. Following this, a thematic analysis was carried out on the acquired data, adhering to the Boyatzis approach.

Results

100 papers were included. The quality and bias risk of the included studies were delineated to be within an acceptable range. Multiple themes were derived from the thematic analysis including: "Prediction of diseases, their diagnosis, and outcomes," "Prediction of organizational issues and attributes," "Prediction of mental health issues and attributes," "Prediction of polypharmacy and emotional analysis of texts," "Prediction of climate change issues and attributes," and "Prediction and identification of success and satisfaction among healthcare individuals."

Conclusion

The findings emphasized AI's significant potential in addressing prevalent healthcare challenges in the Middle East, such as cancer, diabetes, and climate change. AI has the potential to overhaul the healthcare systems. The findings also highlighted the need for policymakers and administrators to develop a concrete plan to effectively integrate AI into healthcare systems.

Using machine learning to identify risk factors for short-term complications following thumb carpometacarpal arthroplasty

Background

Thumb carpometacarpal (CMC) joint osteoarthritis is among the most common degenerative hand diseases. Thumb CMC arthroplasty, or trapeziectomy with or without tendon augmentation, is the most frequently performed surgical treatment and has a strong safety profile. Though adverse outcomes are infrequent, the ability to predict risk for complications has substantial clinical benefits. In the present study, we evaluated a well-known surgical database with machine learning (ML) techniques to predict short-term complications and reoperations after thumb CMC arthroplasty.

Methods

A retrospective study was conducted using data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) years 2005-2020. Outcomes were 30-day wound and medical complications and 30-day return to the operating room. We used three ML algorithms - a Random Forest (RF), Elastic-Net Regression (ENet), and Extreme Gradient Boosted Tree (XGBoost), and a deep learning Neural Network (NN). Feature importance analysis was performed in the highest performing model for each outcome to identify predictors with the greatest contributions.

Results

We included a total of 7711 cases. The RF was the best performing algorithm for all outcomes, with an AUC score of 0.61±0.03 for reoperations, 0.55±0.04 for medical complications, and 0.59±0.03 for wound complications. On feature importance analysis, procedure duration was the highest weighted predictor for reoperations. In all outcomes, procedure duration, older age, and female sex were consistently among the top five predictors.

Conclusions

We successfully developed ML algorithms to predict reoperations, wound complications, and medical complications. RF models had the highest performance in all outcomes.

Artificial intelligence and machine learning in peritoneal dialysis: a systematic review of clinical outcomes and predictive modeling

BACKGROUND

The integration of artificial intelligence (AI) and machine learning (ML) in peritoneal dialysis (PD) presents transformative opportunities for optimizing treatment outcomes and informing clinical decision-making. This study aims to provide a comprehensive overview of the applications of AI/ML techniques in PD, focusing on their potential to predict clinical outcomes and enhance patient care.

MATERIALS AND METHODS

This systematic review was conducted according to PRISMA guidelines (2020), searching key databases for articles on AI and ML applications in PD. The inclusion criteria were stringent, ensuring the selection of high-quality studies. The search strategy comprised MeSH terms and keywords related to PD, AI, and ML. 793 articles were identified, with nine ultimately meeting the inclusion criteria. The review utilized a narrative synthesis approach to summarize findings due to anticipated study heterogeneity.

RESULTS

Nine studies met the inclusion criteria. The studies varied in sample size and employed diverse AI and ML techniques, reflecting the breadth of data considered. Mortality prediction emerged as a recurrent theme, demonstrating the significance of AI and ML in prognostic accuracy. Predictive modeling extended to technique failure, hospital stay prediction, and pathogen-specific immune responses, showcasing the versatility of AI and ML applications in PD.

CONCLUSIONS

This systematic review highlights the diverse applications of AI/ML in peritoneal dialysis, demonstrating their potential to enhance predictive accuracy, risk stratification, and decision support. However, limitations such as small sample sizes, single-center studies, and potential biases warrant further research and external validation. Future perspectives include integrating these AI/ML models into routine clinical practice and exploring additional use cases to improve patient outcomes and healthcare decision-making in PD.

The future of patient education: A study on AI-driven responses to urinary incontinence inquiries

OBJECTIVE

To evaluate the effectiveness of ChatGPT in providing insights into common urinary incontinence concerns within urogynecology. By analyzing the model's responses against established benchmarks of accuracy, completeness, and safety, the study aimed to quantify its usefulness for informing patients and aiding healthcare providers.

METHODS

An expert-driven questionnaire was developed, inviting urogynecologists worldwide to assess ChatGPT's answers to 10 carefully selected questions on urinary incontinence (UI). These assessments focused on the accuracy of the responses, their comprehensiveness, and whether they raised any safety issues. Subsequent statistical analyses determined the average consensus among experts and identified the proportion of responses receiving favorable evaluations (a score of 4 or higher).

RESULTS

Of 50 urogynecologists that were approached worldwide, 37 responded, offering insights into ChatGPT's responses on UI. The overall feedback averaged a score of 4.0, indicating a positive acceptance. Accuracy scores averaged 3.9 with 71% rated favorably, whereas comprehensiveness scored slightly higher at 4 with 74% favorable ratings. Safety assessments also averaged 4 with 74% favorable responses.

CONCLUSION

This investigation underlines ChatGPT's favorable performance across the evaluated domains of accuracy, comprehensiveness, and safety within the context of UI queries. However, despite this broadly positive reception, the study also signals a clear avenue for improvement, particularly in the precision of the provided information. Refining ChatGPT's accuracy and ensuring the delivery of more pinpointed responses are essential steps forward, aiming to bolster its utility as a comprehensive educational resource for patients and a supportive tool for healthcare practitioners.

Student Pharmacists' Perceptions of Artificial Intelligence and Machine Learning in Pharmacy Practice and Pharmacy Education

OBJECTIVE

This study explored student pharmacists' perceptions and attitudes regarding artificial intelligence (AI) and machine learning (ML) in pharmacy practice. Due to AI/ML's promising prospects, understanding students' current awareness, comprehension, and hopes for their use in this field is essential.

METHODS

In April 2024, a Zoom focus group discussion was conducted with 6 student pharmacists using a self-developed interview guide. The guide included questions about the benefits, challenges, and ethical considerations of implementing AI/ML in pharmacy practice and education. The participants' demographic information was collected through a questionnaire. The research team conducted a thematic analysis of the discussion transcript. The results generated by a team member using NVivo were compared with those generated by ChatGPT, and all discrepancies were addressed.

RESULTS

Student pharmacists displayed a generally positive attitude toward the implementation of AI/ML in pharmacy practice but lacked knowledge about AI/ML applications. Participants recognized several advantages of AI/ML implementation in pharmacy practice, including improved accuracy and time-saving for pharmacists. Some identified challenges were alert fatigue, AI/ML-generated errors, and the potential obstacle to person-centered care. The study participants expressed their interest in learning about AI/ML and their desire to integrate these technologies into pharmacy education.

CONCLUSION

The demand for integrating AI/ML into pharmacy practice is increasing. Student and professional pharmacists need additional AI/ML training to equip them with knowledge and practical skills. Collaboration between pharmacists, institutions, and AI/ML companies is essential to address barriers and advance AI/ML implementation in the pharmacy field.

Physicians' ethical concerns about artificial intelligence in medicine: a qualitative study: "The final decision should rest with a human"

Background/aim

Artificial Intelligence (AI) is the capability of computational systems to perform tasks that require human-like cognitive functions, such as reasoning, learning, and decision-making. Unlike human intelligence, AI does not involve sentience or consciousness but focuses on data processing, pattern recognition, and prediction through algorithms and learned experiences. In healthcare including neuroscience, AI is valuable for improving prevention, diagnosis, prognosis, and surveillance.

Methods

This qualitative study aimed to investigate the acceptability of AI in Medicine (AIIM) and to elucidate any technical and scientific, as well as social and ethical issues involved. Twenty-five doctors from various specialties were carefully interviewed regarding their views, experience, knowledge, and attitude toward AI in healthcare.

Results

Content analysis confirmed the key ethical principles involved: confidentiality, beneficence, and non-maleficence. Honesty was the least invoked principle. A thematic analysis established four salient topic areas, i.e., advantages, risks, restrictions, and precautions. Alongside the advantages, there were many limitations and risks. The study revealed a perceived need for precautions to be embedded in healthcare policies to counter the risks discussed. These precautions need to be multi-dimensional.

Conclusion

The authors conclude that AI should be rationally guided, function transparently, and produce impartial results. It should assist human healthcare professionals collaboratively. This kind of AI will permit fairer, more innovative healthcare which benefits patients and society whilst preserving human dignity. It can foster accuracy and precision in medical practice and reduce the workload by assisting physicians during clinical tasks. AIIM that functions transparently and respects the public interest can be an inspiring scientific innovation for humanity.

Data Ownership in the AI-Powered Integrative Health Care Landscape

In the rapidly advancing landscape of artificial intelligence (AI) within integrative health care (IHC), the issue of data ownership has become pivotal. This study explores the intricate dynamics of data ownership in the context of IHC and the AI era, presenting the novel Collaborative Healthcare Data Ownership (CHDO) framework. The analysis delves into the multifaceted nature of data ownership, involving patients, providers, researchers, and AI developers, and addresses challenges such as ambiguous consent, attribution of insights, and international inconsistencies. Examining various ownership models, including privatization and communization postulates, as well as distributed access control, data trusts, and blockchain technology, the study assesses their potential and limitations. The proposed CHDO framework emphasizes shared ownership, defined access and control, and transparent governance, providing a promising avenue for responsible and collaborative AI integration in IHC. This comprehensive analysis offers valuable insights into the complex landscape of data ownership in IHC and the AI era, potentially paving the way for ethical and sustainable advancements in data-driven health care.

The Accuracy and Capability of Artificial Intelligence Solutions in Health Care Examinations and Certificates: Systematic Review and Meta-Analysis

BACKGROUND

Large language models (LLMs) have dominated public interest due to their apparent capability to accurately replicate learned knowledge in narrative text. However, there is a lack of clarity about the accuracy and capability standards of LLMs in health care examinations.

OBJECTIVE

We conducted a systematic review of LLM accuracy, as tested under health care examination conditions, as compared to known human performance standards.

METHODS

We quantified the accuracy of LLMs in responding to health care examination questions and evaluated the consistency and quality of study reporting. The search included all papers up until September 10, 2023, with all LLMs published in English journals that report clear LLM accuracy standards. The exclusion criteria were as follows: the assessment was not a health care exam, there was no LLM, there was no evaluation of comparable success accuracy, and the literature was not original research.The literature search included the following Medical Subject Headings (MeSH) terms used in all possible combinations: "artificial intelligence," "ChatGPT," "GPT," "LLM," "large language model," "machine learning," "neural network," "Generative Pre-trained Transformer," "Generative Transformer," "Generative Language Model," "Generative Model," "medical exam," "healthcare exam," and "clinical exam." Sensitivity, accuracy, and precision data were extracted, including relevant CIs.

RESULTS

The search identified 1673 relevant citations. After removing duplicate results, 1268 (75.8%) papers were screened for titles and abstracts, and 32 (2.5%) studies were included for full-text review. Our meta-analysis suggested that LLMs are able to perform with an overall medical examination accuracy of 0.61 (CI 0.58-0.64) and a United States Medical Licensing Examination (USMLE) accuracy of 0.51 (CI 0.46-0.56), while Chat Generative Pretrained Transformer (ChatGPT) can perform with an overall medical examination accuracy of 0.64 (CI 0.6-0.67).

CONCLUSIONS

LLMs offer promise to remediate health care demand and staffing challenges by providing accurate and efficient context-specific information to critical decision makers. For policy and deployment decisions about LLMs to advance health care, we proposed a new framework called RUBRICC (Regulatory, Usability, Bias, Reliability [Evidence and Safety], Interoperability, Cost, and Codesign-Patient and Public Involvement and Engagement [PPIE]). This presents a valuable opportunity to direct the clinical commissioning of new LLM capabilities into health services, while respecting patient safety considerations.

TRIAL REGISTRATION

OSF Registries osf.io/xqzkw; https://osf.io/xqzkw.

AI-Assisted Detection and Localization of Spinal Metastatic Lesions

OBJECTIVES

The integration of machine learning and radiomics in medical imaging has significantly advanced diagnostic and prognostic capabilities in healthcare. This study focuses on developing and validating an artificial intelligence (AI) model using U-Net architectures for the accurate detection and segmentation of spinal metastases from computed tomography (CT) images, addressing both osteolytic and osteoblastic lesions.

METHODS

Our methodology employs multiple variations of the U-Net architecture and utilizes two distinct datasets: one consisting of 115 polytrauma patients for vertebra segmentation and another comprising 38 patients with documented spinal metastases for lesion detection.

RESULTS

The model demonstrated strong performance in vertebra segmentation, achieving Dice Similarity Coefficient (DSC) values between 0.87 and 0.96. For metastasis segmentation, the model achieved a DSC of 0.71 and an F-beta score of 0.68 for lytic lesions but struggled with sclerotic lesions, obtaining a DSC of 0.61 and an F-beta score of 0.57, reflecting challenges in detecting dense, subtle bone alterations. Despite these limitations, the model successfully identified isolated metastatic lesions beyond the spine, such as in the sternum, indicating potential for broader skeletal metastasis detection.

CONCLUSIONS

The study concludes that AI-based models can augment radiologists' capabilities by providing reliable second-opinion tools, though further refinements and diverse training data are needed for optimal performance, particularly for sclerotic lesion segmentation. The annotated CT dataset produced and shared in this research serves as a valuable resource for future advancements.

The Co-Piloting Model for Using Artificial Intelligence Systems in Medicine: Implementing the Constrained-Disorder-Principle-Based Second-Generation System

The development of artificial intelligence (AI) and machine learning (ML)-based systems in medicine is growing, and these systems are being used for disease diagnosis, drug development, and treatment personalization. Some of these systems are designed to perform activities that demand human cognitive function. However, use of these systems in routine care by patients and caregivers lags behind expectations. This paper reviews several challenges that healthcare systems face and the obstacles of integrating digital systems into routine care. This paper focuses on integrating digital systems with human physicians. It describes second-generation AI systems designed to move closer to biology and reduce complexity, augmenting but not replacing physicians to improve patient outcomes. The constrained disorder principle (CDP) defines complex biological systems by their degree of regulated variability. This paper describes the CDP-based second-generation AI platform, which is the basis for the Digital Pill that is humanizing AI by moving closer to human biology via using the inherent variability of biological systems for improving outcomes. This system augments physicians, assisting them in decision-making to improve patients' responses and adherence but not replacing healthcare providers. It restores the efficacy of chronic drugs and improves adherence while generating data-driven therapeutic regimens. While AI can substitute for many medical activities, it is unlikely to replace human physicians. Human doctors will continue serving patients with capabilities augmented by AI. The described co-piloting model better reflects biological pathways and provides assistance to physicians for better care.

Evaluating the Impact of Artificial Intelligence (AI) on Clinical Documentation Efficiency and Accuracy Across Clinical Settings: A Scoping Review

Artificial intelligence (AI) technologies (natural language processing (NLP), speech recognition (SR), and machine learning (ML)) can transform clinical documentation in healthcare. This scoping review evaluates the impact of AI on the accuracy and efficiency of clinical documentation across various clinical settings (hospital wards, emergency departments, and outpatient clinics). We found 176 articles by applying a specific search string on Ovid. To ensure a more comprehensive search process, we also performed manual searches on PubMed and BMJ, examining any relevant references we encountered. In this way, we were able to add 46 more articles, resulting in 222 articles in total. After removing duplicates, 208 articles were screened. This led to the inclusion of 36 studies. We were mostly interested in articles discussing the impact of AI technologies, such as NLP, ML, and SR, and their accuracy and efficiency in clinical documentation. To ensure that our research reflected recent work, we focused our efforts on studies published in 2019 and beyond. This criterion was pilot-tested beforehand and necessary adjustments were made. After comparing screened articles independently, we ensured inter-rater reliability (Cohen's kappa=1.0), and data extraction was completed on these 36 articles. We conducted this study according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This scoping review shows improvements in clinical documentation using AI technologies, with an emphasis on accuracy and efficiency. There was a reduction in clinician workload, with the streamlining of the documentation processes. Subsequently, doctors also had more time for patient care. However, these articles also raised various challenges surrounding the use of AI in clinical settings. These challenges included the management of errors, legal liability, and integration of AI with electronic health records (EHRs). There were also some ethical concerns regarding the use of AI with patient data. AI shows massive potential for improving the day-to-day work life of doctors across various clinical settings. However, more research is needed to address the many challenges associated with its use. Studies demonstrate improved accuracy and efficiency in clinical documentation with the use of AI. With better regulatory frameworks, implementation, and research, AI can significantly reduce the burden placed on doctors by documentation.

Knowledge, Attitude and Practice of Artificial Intelligence Among Healthcare Professionals at a Tertiary Care Teaching Hospital in South Gujarat

Background Artificial intelligence (AI) is rapidly evolving within healthcare, promising improvements in patient care, diagnostic accuracy, and therapeutic interventions. As AI technology becomes more integrated into clinical workflows, understanding healthcare professionals' (HCPs) knowledge, attitudes, and practices concerning AI is crucial, particularly in diverse healthcare environments like South Gujarat. This study evaluates HCPs' understanding, perception, and application of AI at a tertiary care teaching hospital in this region. Methods This observational, cross-sectional study utilized a non-validated, structured questionnaire based on the Knowledge, Attitude, and Practice (KAP) framework. A convenient sampling technique was employed to recruit 290 HCPs, including consultant doctors, medical faculty, residents, and interns. Data were collected electronically via Google Forms and analyzed using descriptive statistics. Results Most participants (176; 60.7%) were junior residents, with notable representation from departments like Pharmacology and Community Medicine. Regarding AI knowledge, 80 (27.6%) of participants reported full awareness, while 182 (62.8%) were partially aware. AI subtype knowledge varied, with 84 (28.9%) identifying "Self-awareness" and 50 (17.2%) "Limited Memory." Internet sources were the predominant information source for 171 (58.9%) of participants. Notably, 192 (66.2%) recognized AI's role in saving time and enhancing accuracy, although some expressed concerns about its lack of empathy and ethical implications. Conclusions The findings highlight substantial awareness but varying depths of understanding of AI among HCPs, who are interested in further AI education. Increased educational programs on AI's ethical and practical aspects may enhance AI integration into clinical practice, aiding responsible adoption in healthcare settings.

Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: a narrative review

Reports of Large Language Models (LLMs) passing board examinations have spurred medical enthusiasm for their clinical integration. Through a narrative review, we reflect upon the skill shifts necessary for clinicians to succeed in an LLM-enabled world, achieving benefits while minimizing risks. We suggest how medical education must evolve to prepare clinicians capable of navigating human-AI systems.

Application of machine learning based genome sequence analysis in pathogen identification

Infectious diseases caused by pathogenic microorganisms pose a serious threat to human health. Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious diseases remain a significant public health concern. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance requires concerted interdisciplinary efforts. With the development of computer technology and the continuous exploration of artificial intelligence(AI)applications in the biomedical field, the automatic morphological recognition and image processing of microbial images under microscopes have advanced rapidly. The research team of Institute of Microbiology, Chinese Academy of Sciences has developed a single cell microbial identification technology combining Raman spectroscopy and artificial intelligence. Through laser Raman acquisition system and convolutional neural network analysis, the average accuracy rate of 95.64% has been achieved, and the identification can be completed in only 5 min. These technologies have shown substantial advantages in the visible morphological detection of pathogenic microorganisms, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this review, we discuss the application of AI-based machine learning in image analysis, genome sequencing data analysis, and natural language processing (NLP) for pathogen identification, highlighting the significant role of artificial intelligence in pathogen diagnosis. AI can improve the accuracy and efficiency of diagnosis, promote early detection and personalized treatment, and enhance public health safety.

Automatic deep learning detection of overhanging restorations in bitewing radiographs

OBJECTIVES

This study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms for the detecting and segmentation of overhanging dental restorations in bitewing radiographs.

METHODS

A total of 1160 anonymized bitewing radiographs were used to progress the artificial intelligence (AI) system for the detection and segmentation of overhanging restorations. The data were then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels). A CNN model known as You Only Look Once (YOLOv5) was trained to detect overhanging restorations in bitewing radiographs. After utilizing the remaining 115 radiographs to evaluate the efficacy of the proposed CNN model, the accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC) were computed.

RESULTS

The model demonstrated a precision of 90.9%, a sensitivity of 85.3%, and an F1 score of 88.0%. Furthermore, the model achieved an AUC of 0.859 on the receiver operating characteristic (ROC) curve. The mean average precision (mAP) at an intersection over a union (IoU) threshold of 0.5 was notably high at 0.87.

CONCLUSIONS

The findings suggest that deep CNN algorithms are highly effective in the detection and diagnosis of overhanging dental restorations in bitewing radiographs. The high levels of precision, sensitivity, and F1 score, along with the significant AUC and mAP values, underscore the potential of these advanced deep learning techniques in revolutionizing dental diagnostic procedures.

Assessing the response quality and readability of chatbots in cardiovascular health, oncology, and psoriasis: A comparative study

BACKGROUND

Chatbots using the Large Language Model (LLM) generate human responses to questions from all categories. Due to staff shortages in healthcare systems, patients waiting for an appointment increasingly use chatbots to get information about their condition. Given the number of chatbots currently available, assessing the responses they generate is essential.

METHODS

Five chatbots with free access were selected (Gemini, Microsoft Copilot, PiAI, ChatGPT, ChatSpot) and blinded using letters (A, B, C, D, E). Each chatbot was asked questions about cardiology, oncology, and psoriasis. Responses were compared to guidelines from the European Society of Cardiology, American Academy of Dermatology and American Society of Clinical Oncology. All answers were assessed using readability scales (Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level and Dale-Chall Score). Using a 3-point Likert scale, two independent medical professionals assessed the compliance of the responses with the guidelines.

RESULTS

A total of 45 questions were asked of all chatbots. Chatbot C gave the shortest answers, 7.0 (6.0 - 8.0), and Chatbot A the longest 17.5 (13.0 - 24.5). The Flesch Reading Ease Scale ranged from 16.3 (12.2 - 21.9) (Chatbot D) to 39.8 (29.0 - 50.4) (Chatbot A). Flesch-Kincaid Grade Level ranged from 12.5 (10.6 - 14.6) (Chatbot A) to 15.9 (15.1 - 17.1) (Chatbot D). Gunning Fog Scale Level ranged from 15.77 (Chatbot A) to 19.73 (Chatbot D). Dale-Chall Score ranged from 10.3 (9.3 - 11.3) (Chatbot A) to 11.9 (11.5 - 12.4) (Chatbot D).

CONCLUSION

This study indicates that chatbots vary in length, quality, and readability. They answer each question in their own way, based on the data they have pulled from the web. Reliability of the responses generated by chatbots is high. This suggests that people who want information from a chatbot need to be careful and verify the answers they receive, particularly when they ask about medical and health aspects.

Deep-learning generated B-line score mirrors clinical progression of disease for patients with heart failure

BACKGROUND

Ultrasound can detect fluid in the alveolar and interstitial spaces of the lung using the presence of artifacts known as B-lines. The aim of this study was to determine whether a deep learning algorithm generated B-line severity score correlated with pulmonary congestion and disease severity based on clinical assessment (as identified by composite congestion score and Rothman index) and to evaluate changes in the score with treatment. Patients suspected of congestive heart failure underwent daily ultrasonography. Eight lung zones (right and left anterior/lateral and superior/inferior) were scanned using a tablet ultrasound system with a phased-array probe. Mixed effects modeling explored the association between average B-line score and the composite congestion score, and average B-line score and Rothman index, respectively. Covariates tested included patient and exam level data (sex, age, presence of selected comorbidities, baseline sodium and hemoglobin, creatinine, vital signs, oxygen delivery amount and delivery method, diuretic dose).

RESULTS

Analysis included 110 unique subjects (3379 clips). B-line severity score was significantly associated with the composite congestion score, with a coefficient of 0.7 (95% CI 0.1-1.2 p = 0.02), but was not significantly associated with the Rothman index.

CONCLUSIONS

Use of this technology may allow clinicians with limited ultrasound experience to determine an objective measure of B-line burden.

Gemini AI vs. ChatGPT: A comprehensive examination alongside ophthalmology residents in medical knowledge

INTRODUCTION

The rapid advancement of artificial intelligence (AI), particularly in large language models like ChatGPT and Google's Gemini AI, marks a transformative era in technological innovation. This study explores the potential of AI in ophthalmology, focusing on the capabilities of ChatGPT and Gemini AI. While these models hold promise for medical education and clinical support, their integration requires comprehensive evaluation. This research aims to bridge a gap in the literature by comparing Gemini AI and ChatGPT, assessing their performance against ophthalmology residents using a dataset derived from ophthalmology board exams.

METHODS

A dataset comprising 600 questions across 12 subspecialties was curated from Israeli ophthalmology residency exams, encompassing text and image-based formats. Four AI models - ChatGPT-3.5, ChatGPT-4, Gemini, and Gemini Advanced - underwent testing on this dataset. The study includes a comparative analysis with Israeli ophthalmology residents, employing specific metrics for performance assessment.

RESULTS

Gemini Advanced demonstrated superior performance with a 66% accuracy rate. Notably, ChatGPT-4 exhibited improvement at 62%, Gemini at 58%, and ChatGPT-3.5 served as the reference at 46%. Comparative analysis with residents offered insights into AI models' performance relative to human-level medical knowledge. Further analysis delved into yearly performance trends, topic-specific variations, and the impact of images on chatbot accuracy.

CONCLUSION

The study unveils nuanced AI model capabilities in ophthalmology, emphasizing domain-specific variations. The superior performance of Gemini Advanced superior performance indicates significant advancements, while ChatGPT-4's improvement is noteworthy. Both Gemini and ChatGPT-3.5 demonstrated commendable performance. The comparative analysis underscores AI's evolving role as a supplementary tool in medical education. This research contributes vital insights into AI effectiveness in ophthalmology, highlighting areas for refinement. As AI models evolve, targeted improvements can enhance adaptability across subspecialties, making them valuable tools for medical professionals and enriching patient care.

KEY MESSAGES

What is known AI breakthroughs, like ChatGPT and Google's Gemini AI, are reshaping healthcare. In ophthalmology, AI integration has overhauled clinical workflows, particularly in analyzing images for diseases like diabetic retinopathy and glaucoma. What is new This study presents a pioneering comparison between Gemini AI and ChatGPT, evaluating their performance against ophthalmology residents using a meticulously curated dataset derived from real-world ophthalmology board exams. Notably, Gemini Advanced demonstrates superior performance, showcasing substantial advancements, while the evolution of ChatGPT-4 also merits attention. Both models exhibit commendable capabilities. These findings offer crucial insights into the efficacy of AI in ophthalmology, shedding light on areas ripe for further enhancement and optimization.

Artificial Intelligence-Based Facial Palsy Evaluation: A Survey

Facial palsy evaluation (FPE) aims to assess facial palsy severity of patients, which plays a vital role in facial functional treatment and rehabilitation. The traditional manners of FPE are based on subjective judgment by clinicians, which may ultimately depend on individual experience. Compared with subjective and manual evaluation, objective and automated evaluation using artificial intelligence (AI) has shown great promise in improving traditional manners and recently received significant attention. The motivation of this survey paper is mainly to provide a systemic review that would guide researchers in conducting their future research work and thus make automatic FPE applicable in real-life situations. In this survey, we comprehensively review the state-of-the-art development of AI-based FPE. First, we summarize the general pipeline of FPE systems with the related background introduction. Following this pipeline, we introduce the existing public databases and give the widely used objective evaluation metrics of FPE. In addition, the preprocessing methods in FPE are described. Then, we provide an overview of selected key publications from 2008 and summarize the state-of-the-art methods of FPE that are designed based on AI techniques. Finally, we extensively discuss the current research challenges faced by FPE and provide insights about potential future directions for advancing state-of-the-art research in this field.

Harnessing Corneal Stromal Regeneration for Vision Restoration: A Comprehensive Review of the Emerging Treatment Techniques for Keratoconus

Keratoconus is a progressive corneal disorder characterized by thinning and conical protrusion, leading to visual impairment that often necessitates advanced treatment strategies. Traditional management options, including corrective lenses, corneal cross-linking (CXL), and surgical interventions such as corneal transplants and intracorneal ring segments (ICRS), address symptoms but have limitations, especially in progressive or advanced cases. Recent advancements in corneal stromal regeneration offer promising alternatives for enhancing vision restoration and halting disease progression. This review explores emerging techniques focused on corneal stromal regeneration, emphasizing cell-based therapies, tissue engineering, and gene therapy. Cell-based approaches, including corneal stromal stem cells and adipose-derived stem cells, are promising to promote tissue repair and functional recovery. Tissue engineering techniques, such as developing synthetic and biological scaffolds and 3D bioprinting, are being investigated for their ability to create viable corneal grafts and implants. Additionally, gene therapy and molecular strategies, including gene editing technologies and the application of growth factors, are advancing the potential for targeted treatment and regenerative medicine. Despite these advancements, challenges remain, including technical limitations, safety concerns, and ethical considerations. This review aims to provide a comprehensive overview of these innovative approaches, highlighting their current status, clinical outcomes, and future directions in keratoconus management.

Beyond Boundaries: The Role of Artificial Intelligence in Shaping the Future Careers of Medical Students in Saudi Arabia

INTRODUCTION

Artificial intelligence (AI) stands at the forefront of revolutionizing healthcare, wielding its computational prowess to navigate the labyrinth of medical data with unprecedented precision. In this study, we delved into the perspectives of medical students in the Kingdom of Saudi Arabia (KSA) regarding AI's seismic impact on their careers and the medical landscape.

METHODS

 A cross-sectional study conducted from February to December 2023 examined the impact of AI on the future of medical students' careers in KSA, surveying approximately 400 participants, including Saudi medical students and interns, and uncovering a fascinating tapestry of perceptions.

RESULTS

Astonishingly, 75.4% of respondents boasted familiarity with AI, heralding its transformative potential. A resounding 88.9% lauded its capacity to enrich medical education, marking a paradigm shift in learning approaches. However, amidst this wave of optimism, shadows of apprehension loomed. A staggering 42.5% harbored concerns of AI precipitating job displacement, while 34.4% envisioned a future where AI usurps traditional doctor roles. Despite this dichotomy, there existed a unanimous recognition of the symbiotic relationship between AI and human healthcare professionals, heralding an era of collaborative synergy.

CONCLUSION

Our findings underscored a critical need for educational initiatives to assuage fears and facilitate the seamless integration of AI into clinical practice. Moreover, AI's burgeoning influence in diagnostic radiology and personalized healthcare plans emerged as catalysts propelling the domain of precision medicine into uncharted realms of innovation. As AI reshapes the contours of healthcare delivery, it not only promises unparalleled efficiency but also holds the key to unlocking new frontiers in treatment outcomes and accessibility, heralding a transformative epoch in the annals of medicine.

Toward Fully Automated Personalized Orthopedic Treatments: Innovations and Interdisciplinary Gaps

Personalized orthopedic devices are increasingly favored for their potential to enhance long-term treatment success. Despite significant advancements across various disciplines, the seamless integration and full automation of personalized orthopedic treatments remain elusive. This paper identifies key interdisciplinary gaps in integrating and automating advanced technologies for personalized orthopedic treatment. It begins by outlining the standard clinical practices in orthopedic treatments and the extent of personalization achievable. The paper then explores recent innovations in artificial intelligence, biomaterials, genomic and proteomic analyses, lab-on-a-chip, medical imaging, image-based biomechanical finite element modeling, biomimicry, 3D printing and bioprinting, and implantable sensors, emphasizing their contributions to personalized treatments. Tentative strategies or solutions are proposed to address the interdisciplinary gaps by utilizing innovative technologies. The key findings highlight the need for the non-invasive quantitative assessment of bone quality, patient-specific biocompatibility, and device designs that address individual biological and mechanical conditions. This comprehensive review underscores the transformative potential of these technologies and the importance of multidisciplinary collaboration to integrate and automate them into a cohesive, intelligent system for personalized orthopedic treatments.

Revolutionizing Intracranial Hemorrhage Diagnosis: A Retrospective Analytical Study of Viz.ai ICH for Enhanced Diagnostic Accuracy

Introduction Artificial intelligence (AI) alerts the radiologist to the presence of intracranial hemorrhage (ICH) as fast as 1-2 minutes from scan completion, leading to faster diagnosis and treatment. We wanted to validate a new AI application called Viz.ai ICH to improve the diagnosis of suspected ICH. Methods We performed a retrospective analysis of 4,203 consecutive non-contrast brain computed tomography (CT) reports in a single institution between September 1, 2021, and January 31, 2022. The reports were made by neuroradiologists who reviewed each case for the presence of ICH. Reports and identified cases with positive findings for ICH were reviewed. Positive cases were categorized based on subtype, timing, and size/volume. Viz.ai ICH output was reviewed for positive cases. This AI model was validated by assessing its performance with Viz.ai ICH as the index test compared to the neuroradiologists' interpretation as the gold standard. Results According to neuroradiologists, 9.2% of non-contrast brain CT reports were positive for ICH. The sensitivity of Viz.ai ICH was 85%, specificity was 98%, positive predictive value was 81%, and negative predictive value was 99%. Subgroup analysis was performed based on intraparenchymal, subarachnoid, subdural, and intraventricular subtypes. Sensitivities were 94%, 79%, 83%, and 44%, respectively. Further stratification revealed sensitivity improves with higher acuity and volume/size across subtypes. Conclusion Our analysis indicates that AI can accurately detect ICH's presence, particularly for large-volume/large-size ICH. The paper introduces a novel AI model for detecting ICH. This advancement contributes to the field by revolutionizing ICH detection and improving patient outcomes.

Knowledge and acceptance of artificial intelligence and its applications among the physicians working in military medical centers affiliated with Aja University: A cross-sectional study

BACKGROUND

The use of artificial intelligence (AI) in medical sciences promises many benefits. Applying the benefits of this science in developing countries is still in the development stage. This important point depends considerably on the knowledge and acceptance levels of physicians.

MATERIALS AND METHODS

This study was a cross-sectional descriptive-analytical study that was conducted on 169 medical doctors using a purposive sampling method. To collect data, questionnaires were used to obtain demographic characteristics, a questionnaire to investigate the knowledge of AI and its applications, and an acceptability questionnaire to investigate AI. For data analysis, SPSS (Statistical Package for the Social Sciences) version 22 and appropriate descriptive and inferential statistical tests were used, and a significance level of < 0.05 was considered.

RESULTS

Most of the participants (102) were male (60.4%), married (144) (85.20%), had specialized doctorate education (97) (57.4%), and had average work experience of 10.78 ± 6.67 years. The mean and standard deviation of knowledge about AI were 9.54 ± 3.04, and acceptability was 81.64 ± 13.83. Multiple linear regressions showed that work history (P = 0.017) and history of participation in AI training courses (P = 0.007) are effective in knowledge and acceptability of AI.

CONCLUSION

The knowledge and acceptability of the use of AI among the studied physicians were at an average level. However, due to the importance of using AI in medical sciences and the inevitable use of this technology in the near future, especially in medical sciences in crisis, war, and military conditions, it is necessary for the policymakers of the health system to improve the knowledge and methods of working with this technology in the medical staff in addition to providing the infrastructure.

Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review

BACKGROUND

The successful integration of artificial intelligence (AI) into clinical practice is contingent upon physicians' comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process.

OBJECTIVE

The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians.

METHODS

We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results.

RESULTS

Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs.

CONCLUSIONS

This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.11124/JBIES-22-00374.

Artificial intelligence in perinatal mental health research: A scoping review

The intersection of Artificial Intelligence (AI) and perinatal mental health research presents promising avenues, yet uncovers significant challenges for innovation. This review explicitly focuses on this multidisciplinary field and undertakes a comprehensive exploration of existing research therein. Through a scoping review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we searched relevant literature spanning a decade (2013-2023) and selected fourteen studies for our analysis. We first provide an overview of the main AI techniques and their development, including traditional methods across different categories, as well as recent emerging methods in the field. Then, through our analysis of the literature, we summarize the predominant AI and ML techniques adopted and their applications in perinatal mental health studies, such as identifying risk factors, predicting perinatal mental health disorders, voice assistants, and Q&A chatbots. We also discuss existing limitations and potential challenges that hinder AI technologies from improving perinatal mental health outcomes, and suggest several promising directions for future research to meet real needs in the field and facilitate the translation of research into clinical settings.

Adjunctive Testing Using Biospectral Emission Sequencing: Bioregulatory Intelligence Technology in Parallel With the Goals of Artificial Intelligence in Medicine

The many advancements in medical technology of the last century have continually sought to improve the sensitivity of testing and the specificity of treatment of human maladies. Conventional physical and pharmaceutical treatment is largely an imprecise process, stimulating the impetus for the advancement of machine learning-enhanced artificial intelligence (AI) medical technologies. Biospectral Emission Sequencing (BES) is a bioregulatory intelligence (BI) technology already in use as an adjunct to conventional testing. Biospectral Emission Sequencing provides a functional system of dynamic real-time adjunctive testing and treatment selection. This paper discusses the parallel technologies of present and future AI and BI technologies in medicine.

The Utility of Artificial Intelligence and Machine Learning in the Diagnosis of Takotsubo Cardiomyopathy: A Systematic Review

Introduction:

Takotsubo cardiomyopathy (TTC) is a cardiovascular disease caused by physical/psychological stressors with significant morbidity if left untreated. Because TTC often mimics acute myocardial infarction in the absence of obstructive coronary disease, the condition is often underdiagnosed in the population. Our aim was to discuss the role of artificial intelligence (AI) and machine learning (ML) in diagnosing TTC.

Methods:

We systematically searched electronic databases from inception until April 8, 2023, for studies on the utility of AI- or ML-based algorithms in diagnosing TTC compared with other cardiovascular diseases or healthy controls. We summarized major findings in a narrative fashion and tabulated relevant numerical parameters.

Results:

Five studies with a total of 920 patients were included. Four hundred and forty-seven were diagnosed with TTC via International Classification of Diseases codes or the Mayo Clinic diagnostic criteria, while there were 473 patients in the comparator group (29 of healthy controls, 429 of myocardial infarction, and 14 of acute myocarditis). Hypertension and smoking were the most common comorbidities in both cohorts, but there were no statistical differences between TTC and comparators. Two studies utilized deep-learning algorithms on transthoracic echocardiographic images, while the rest incorporated supervised ML on cardiac magnetic resonance imaging, 12-lead electrocardiographs, and brain magnetic resonance imaging. All studies found that AI-based algorithms can increase the diagnostic rate of TTC when compared to healthy controls or myocardial infarction patients. In three of these studies, AI-based algorithms had higher sensitivity and specificity compared to human readers.

Conclusion:

AI and ML algorithms can improve the diagnostic capacity of TTC and additionally reduce erroneous human error in differentiating from MI and healthy individuals.

Diagnosis to dissection: AI's role in early detection and surgical intervention for gastric cancer

Gastric cancer remains a formidable health challenge worldwide; early detection and effective surgical intervention are critical for improving patient outcomes. This comprehensive review explores the evolving landscape of gastric cancer management, emphasizing the significant contributions of artificial intelligence (AI) in revolutionizing both diagnostic and therapeutic approaches. Despite advancements in the medical field, the subtle nature of early gastric cancer symptoms often leads to late-stage diagnoses, where survival rates are notably decreased. Historically, the treatment of gastric cancer has transitioned from palliative care to surgical resection, evolving further with the introduction of minimally invasive surgical (MIS) techniques. In the current era, AI has emerged as a transformative force, enhancing the precision of early gastric cancer detection through sophisticated image analysis, and supporting surgical decision-making with predictive modeling and real-time preop-, intraop-, and postoperative guidance. However, the deployment of AI in healthcare raises significant ethical, legal, and practical challenges, including the necessity for ongoing professional education and the development of standardized protocols to ensure patient safety and the effective use of AI technologies. Future directions point toward a synergistic integration of AI with clinical best practices, promising a new era of personalized, efficient, and safer gastric cancer management.

Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education

Background

The increasing importance of artificial intelligence (AI) in health care has generated a growing need for health care professionals to possess a comprehensive understanding of AI technologies, requiring an adaptation in medical education.

Objective

This paper explores stakeholder perceptions and expectations regarding AI in medicine and examines their potential impact on the medical curriculum. This study project aims to assess the AI experiences and awareness of different stakeholders and identify essential AI-related topics in medical education to define necessary competencies for students.

Methods

The empirical data were collected as part of the TüKITZMed project between August 2022 and March 2023, using a semistructured qualitative interview. These interviews were administered to a diverse group of stakeholders to explore their experiences and perspectives of AI in medicine. A qualitative content analysis of the collected data was conducted using MAXQDA software.

Results

Semistructured interviews were conducted with 38 participants (6 lecturers, 9 clinicians, 10 students, 6 AI experts, and 7 institutional stakeholders). The qualitative content analysis revealed 6 primary categories with a total of 24 subcategories to answer the research questions. The evaluation of the stakeholders' statements revealed several commonalities and differences regarding their understanding of AI. Crucial identified AI themes based on the main categories were as follows: possible curriculum contents, skills, and competencies; programming skills; curriculum scope; and curriculum structure.

Conclusions

The analysis emphasizes integrating AI into medical curricula to ensure students' proficiency in clinical applications. Standardized AI comprehension is crucial for defining and teaching relevant content. Considering diverse perspectives in implementation is essential to comprehensively define AI in the medical context, addressing gaps and facilitating effective solutions for future AI use in medical studies. The results provide insights into potential curriculum content and structure, including aspects of AI in medicine.

Chinese Oncologists' Perspectives on Integrating AI into Clinical Practice: Cross-Sectional Survey Study

BACKGROUND

The rapid development of artificial intelligence (AI) has brought significant interest to its potential applications in oncology. Although AI-powered tools are already being implemented in some Chinese hospitals, their integration into clinical practice raises several concerns for Chinese oncologists.

OBJECTIVE

This study aims to explore the concerns of Chinese oncologists regarding the integration of AI into clinical practice and to identify the factors influencing these concerns.

METHODS

A total of 228 Chinese oncologists participated in a cross-sectional web-based survey from April to June in 2023 in mainland China. The survey gauged their worries about AI with multiple-choice questions. The survey evaluated their views on the statements of "The impact of AI on the doctor-patient relationship" and "AI will replace doctors." The data were analyzed using descriptive statistics, and variate analyses were used to find correlations between the oncologists' backgrounds and their concerns.

RESULTS

The study revealed that the most prominent concerns were the potential for AI to mislead diagnosis and treatment (163/228, 71.5%); an overreliance on AI (162/228, 71%); data and algorithm bias (123/228, 54%); issues with data security and patient privacy (123/228, 54%); and a lag in the adaptation of laws, regulations, and policies in keeping up with AI's development (115/228, 50.4%). Oncologists with a bachelor's degree expressed heightened concerns related to data and algorithm bias (34/49, 69%; P=.03) and the lagging nature of legal, regulatory, and policy issues (32/49, 65%; P=.046). Regarding AI's impact on doctor-patient relationships, 53.1% (121/228) saw a positive impact, whereas 35.5% (81/228) found it difficult to judge, 9.2% (21/228) feared increased disputes, and 2.2% (5/228) believed that there is no impact. Although sex differences were not significant (P=.08), perceptions varied-male oncologists tended to be more positive than female oncologists (74/135, 54.8% vs 47/93, 50%). Oncologists with a bachelor's degree (26/49, 53%; P=.03) and experienced clinicians (≥21 years; 28/56, 50%; P=.054). found it the hardest to judge. Those with IT experience were significantly more positive (25/35, 71%) than those without (96/193, 49.7%; P=.02). Opinions regarding the possibility of AI replacing doctors were diverse, with 23.2% (53/228) strongly disagreeing, 14% (32/228) disagreeing, 29.8% (68/228) being neutral, 16.2% (37/228) agreeing, and 16.7% (38/228) strongly agreeing. There were no significant correlations with demographic and professional factors (all P>.05).

CONCLUSIONS

Addressing oncologists' concerns about AI requires collaborative efforts from policy makers, developers, health care professionals, and legal experts. Emphasizing transparency, human-centered design, bias mitigation, and education about AI's potential and limitations is crucial. Through close collaboration and a multidisciplinary strategy, AI can be effectively integrated into oncology, balancing benefits with ethical considerations and enhancing patient care.

Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review

Pain is a significant health issue, and pain assessment is essential for proper diagnosis, follow-up, and effective management of pain. The conventional methods of pain assessment often suffer from subjectivity and variability. The main issue is to understand better how people experience pain. In recent years, artificial intelligence (AI) has been playing a growing role in improving clinical diagnosis and decision-making. The application of AI offers promising opportunities to improve the accuracy and efficiency of pain assessment. This review article provides an overview of the current state of AI in pain assessment and explores its potential for improving accuracy, efficiency, and personalized care. By examining the existing literature, research gaps, and future directions, this article aims to guide further advancements in the field of pain management. An online database search was conducted via multiple websites to identify the relevant articles. The inclusion criteria were English articles published between January 2014 and January 2024). Articles that were available as full text clinical trials, observational studies, review articles, systemic reviews, and meta-analyses were included in this review. The exclusion criteria were articles that were not in the English language, not available as free full text, those involving pediatric patients, case reports, and editorials. A total of (47) articles were included in this review. In conclusion, the application of AI in pain management could present promising solutions for pain assessment. AI can potentially increase the accuracy, precision, and efficiency of objective pain assessment.

Revolutionizing Neurology: The Role of Artificial Intelligence in Advancing Diagnosis and Treatment

Artificial intelligence (AI) has emerged as a powerful tool in the field of neurology, significantly impacting the diagnosis and treatment of neurological disorders. Recent technological breakthroughs have given us access to a plethora of information relevant to many aspects of neurology. Neuroscience and AI share a long history of collaboration. Along with great potential, we encounter obstacles relating to data quality, ethics, and inherent difficulty in applying data science in healthcare. Neurological disorders pose intricate challenges due to their complex manifestations and variability. Automating image interpretation tasks, AI algorithms accurately identify brain structures and detect abnormalities. This accelerates diagnosis and reduces the workload on medical professionals. Treatment optimization benefits from AI simulations that model different scenarios and predict outcomes. These AI systems can currently perform many of the sophisticated perceptual and cognitive capacities of biological systems, such as object identification and decision making. Furthermore, AI is rapidly being used as a tool in neuroscience research, altering our understanding of brain functioning. It has the ability to revolutionize healthcare as we know it into a system in which humans and robots collaborate to deliver better care for our patients. Image analysis activities such as recognizing particular brain regions, calculating changes in brain volume over time, and detecting abnormalities in brain scans can be automated by AI systems. This lessens the strain on radiologists and neurologists while improving diagnostic accuracy and efficiency. It is now obvious that cutting-edge artificial intelligence models combined with high-quality clinical data will lead to enhanced prognostic and diagnostic models in neurological illness, permitting expert-level clinical decision aids across healthcare settings. In conclusion, AI's integration into neurology has revolutionized diagnosis, treatment, and research. As AI technologies advance, they promise to unravel the complexities of neurological disorders further, leading to improved patient care and quality of life. The symbiosis of AI and neurology offers a glimpse into a future where innovation and compassion converge to reshape neurological healthcare. This abstract provides a concise overview of the role of AI in neurology and its transformative potential.

The Use of Deep Learning Software in the Detection of Voice Disorders: A Systematic Review

OBJECTIVE

To summarize the use of deep learning in the detection of voice disorders using acoustic and laryngoscopic input, compare specific neural networks in terms of accuracy, and assess their effectiveness compared to expert clinical visual examination.

DATA SOURCES

Embase, MEDLINE, and Cochrane Central.

REVIEW METHODS

Databases were screened through November 11, 2023 for relevant studies. The inclusion criteria required studies to utilize a specified deep learning method, use laryngoscopy or acoustic input, and measure accuracy of binary classification between healthy patients and those with voice disorders.

RESULTS

Thirty-four studies met the inclusion criteria, with 18 focusing on voice analysis, 15 on imaging analysis, and 1 both. Across the 18 acoustic studies, 21 programs were used for identification of organic and functional voice disorders. These technologies included 10 convolutional neural networks (CNNs), 6 multilayer perceptrons (MLPs), and 5 other neural networks. The binary classification systems yielded a mean accuracy of 89.0% overall, including 93.7% for MLP programs and 84.5% for CNNs. Among the 15 imaging analysis studies, a total of 23 programs were utilized, resulting in a mean accuracy of 91.3%. Specifically, the twenty CNNs achieved a mean accuracy of 92.6% compared to 83.0% for the 3 MLPs.

CONCLUSION

Deep learning models were shown to be highly accurate in the detection of voice pathology, with CNNs most effective for assessing laryngoscopy images and MLPs most effective for assessing acoustic input. While deep learning methods outperformed expert clinical exam in limited comparisons, further studies integrating external validation are necessary.

Screening/diagnosis of pediatric endocrine disorders through the artificial intelligence model in different language settings

This study is aimed at examining the impact of ChatGPT on pediatric endocrine and metabolic conditions, particularly in the areas of screening and diagnosis, in both Chinese and English modes. A 40-question questionnaire covering the four most common pediatric endocrine and metabolic conditions was posed to ChatGPT in both Chinese and English three times each. Six pediatric endocrinologists evaluated the responses. ChatGPT performed better when responding to questions in English, with an unreliable rate of 7.5% compared to 27.5% for Chinese questions, indicating a more consistent response pattern in English. Among the reliable questions, the answers were more comprehensive and satisfactory in the English mode. We also found disparities in ChatGPT's performance when interacting with different target groups and diseases, with improved performance for questions posed by clinicians in English and better performance for questions related to diabetes and overweight/obesity in Chinese for both clinicians and patients. Language comprehension, providing incomprehensive answers, and errors in key data were the main contributors to the low scores, according to reviewer feedback.

CONCLUSION

Despite these limitations, as ChatGPT continues to evolve and expand its network, it has significant potential as a practical and effective tool for clinical diagnosis and treatment.

WHAT IS KNOWN

• The deep learning-based large-language model ChatGPT holds great promise for improving clinical practice for both physicians and patients and has the potential to increase the speed and accuracy of disease screening and diagnosis, as well as enhance the overall efficiency of the medical process. However, the reliability and appropriateness of AI model responses in specific field remains unclear. • This study focused on the reliability and appropriateness of AI model responses to straightforward and fundamental questions related to the four most prevalent pediatric endocrine and metabolic disorders, for both healthcare providers and patients, in different language scenarios.

WHAT IS NEW

• The AI model performed better when responding to questions in English, with more consistent, as well as more comprehensive and satisfactory responses. In addition, we also found disparities in ChatGPT's performance when interacting with different target groups and different diseases. • Despite these limitations, as ChatGPT continues to evolve and expand its network, it has significant potential as a practical and effective tool for clinical diagnosis and treatment.

Role of Robotic-Assisted Surgery in Public Health: Its Advantages and Challenges

The modern hospital setting is closely related to engineering and technology. In a hospital, modern equipment is abundant in every department, including the operating room, intensive care unit, and laboratories. Thus, the quality of treatment provided in hospitals and technology advancements are closely tied. Robotic systems are used to support and improve the accuracy and agility of human surgeons during medical procedures. This surgical approach is commonly referred to as robotic surgery or robotic-assisted surgery (RAS). These systems are not entirely autonomous; they are managed by skilled surgeons who carry out procedures with improved accuracy and minimized invasiveness using a console and specialized instruments. Because RAS offers increased surgical precision, less discomfort after surgery, shorter hospital stays, and faster recovery time, all of which improve patient outcomes and lessen the strain on healthcare resources, it plays a critical role in public health. Its minimally invasive technique benefits patients and the healthcare system by lowering problems, reducing the requirement for blood transfusions, and reducing the danger of infections related to medical care. Furthermore, the possibility of remote surgery via robotic systems can increase access to specialized care, reducing regional differences and advancing fairness in public health. In this review article, we will be covering how RAS has its role in public health.

The role of artificial intelligence for the application of integrating electronic health records and patient-generated data in clinical decision support

This narrative review aims to identify and understand the role of artificial intelligence in the application of integrated electronic health records (EHRs) and patient-generated health data (PGHD) in clinical decision support. We focused on integrated data that combined PGHD and EHR data, and we investigated the role of artificial intelligence (AI) in the application. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search articles in six databases: PubMed, Embase, Web of Science, Scopus, ACM Digital Library, and IEEE Computer Society Digital Library. In addition, we also synthesized seminal sources, including other systematic reviews, reports, and white papers, to inform the context, history, and development of this field. Twenty-six publications met the review criteria after screening. The EHR-integrated PGHD introduces benefits to health care, including empowering patients and families to engage via shared decision-making, improving the patient-provider relationship, and reducing the time and cost of clinical visits. AI's roles include cleaning and management of heterogeneous datasets, assisting in identifying dynamic patterns to improve clinical care processes, and providing more sophisticated algorithms to better predict outcomes and propose precise recommendations based on the integrated data. Challenges mainly stem from the large volume of integrated data, data standards, data exchange and interoperability, security and privacy, interpretation, and meaningful use. The use of PGHD in health care is at a promising stage but needs further work for widespread adoption and seamless integration into health care systems. AI-driven, EHR-integrated PGHD systems can greatly improve clinicians' abilities to diagnose patients' health issues, classify risks at the patient level by drawing on the power of integrated data, and provide much-needed support to clinics and hospitals. With EHR-integrated PGHD, AI can help transform health care by improving diagnosis, treatment, and the delivery of clinical care, thus improving clinical decision support.

The Effect of Artificial Intelligence on Patient-Physician Trust: Cross-Sectional Vignette Study

BACKGROUND

Clinical decision support systems (CDSSs) based on routine care data, using artificial intelligence (AI), are increasingly being developed. Previous studies focused largely on the technical aspects of using AI, but the acceptability of these technologies by patients remains unclear.

OBJECTIVE

We aimed to investigate whether patient-physician trust is affected when medical decision-making is supported by a CDSS.

METHODS

We conducted a vignette study among the patient panel (N=860) of the University Medical Center Utrecht, the Netherlands. Patients were randomly assigned into 4 groups-either the intervention or control groups of the high-risk or low-risk cases. In both the high-risk and low-risk case groups, a physician made a treatment decision with (intervention groups) or without (control groups) the support of a CDSS. Using a questionnaire with a 7-point Likert scale, with 1 indicating "strongly disagree" and 7 indicating "strongly agree," we collected data on patient-physician trust in 3 dimensions: competence, integrity, and benevolence. We assessed differences in patient-physician trust between the control and intervention groups per case using Mann-Whitney U tests and potential effect modification by the participant's sex, age, education level, general trust in health care, and general trust in technology using multivariate analyses of (co)variance.

RESULTS

In total, 398 patients participated. In the high-risk case, median perceived competence and integrity were lower in the intervention group compared to the control group but not statistically significant (5.8 vs 5.6; P=.16 and 6.3 vs 6.0; P=.06, respectively). However, the effect of a CDSS application on the perceived competence of the physician depended on the participant's sex (P=.03). Although no between-group differences were found in men, in women, the perception of the physician's competence and integrity was significantly lower in the intervention compared to the control group (P=.009 and P=.01, respectively). In the low-risk case, no differences in trust between the groups were found. However, increased trust in technology positively influenced the perceived benevolence and integrity in the low-risk case (P=.009 and P=.04, respectively).

CONCLUSIONS

We found that, in general, patient-physician trust was high. However, our findings indicate a potentially negative effect of AI applications on the patient-physician relationship, especially among women and in high-risk situations. Trust in technology, in general, might increase the likelihood of embracing the use of CDSSs by treating professionals.

Suitability of the Current Health Technology Assessment of Innovative Artificial Intelligence-Based Medical Devices: Scoping Literature Review

BACKGROUND

Artificial intelligence (AI)-based medical devices have garnered attention due to their ability to revolutionize medicine. Their health technology assessment framework is lacking.

OBJECTIVE

This study aims to analyze the suitability of each health technology assessment (HTA) domain for the assessment of AI-based medical devices.

METHODS

We conducted a scoping literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We searched databases (PubMed, Embase, and Cochrane Library), gray literature, and HTA agency websites.

RESULTS

A total of 10.1% (78/775) of the references were included. Data quality and integration are vital aspects to consider when describing and assessing the technical characteristics of AI-based medical devices during an HTA process. When it comes to implementing specialized HTA for AI-based medical devices, several practical challenges and potential barriers could be highlighted and should be taken into account (AI technological evolution timeline, data requirements, complexity and transparency, clinical validation and safety requirements, regulatory and ethical considerations, and economic evaluation).

CONCLUSIONS

The adaptation of the HTA process through a methodological framework for AI-based medical devices enhances the comparability of results across different evaluations and jurisdictions. By defining the necessary expertise, the framework supports the development of a skilled workforce capable of conducting robust and reliable HTAs of AI-based medical devices. A comprehensive adapted HTA framework for AI-based medical devices can provide valuable insights into the effectiveness, cost-effectiveness, and societal impact of AI-based medical devices, guiding their responsible implementation and maximizing their benefits for patients and health care systems.

Development and validation of a machine learning model to improve precision prediction for irrational prescriptions in orthopedic perioperative patients

OBJECTIVE

Our objective was to develop a machine learning model capable of predicting irrational medical prescriptions precisely within orthopedic perioperative patients.

METHODS

A dataset comprising 3047 instances of suspected irrational medication prescriptions was collected from a sample of 1318 orthopedic perioperative patients from April 2019 to March 2022. Four machine learning models were employed to forecast irrational prescriptions, following which, the performance of each model was meticulously assessed. Subsequently, a thorough variable importance analysis was conducted on the model that performed the best predictive capabilities. Thereafter, the efficacy of integrating this optimal model into the existing audit prescription process was rigorously evaluated.

RESULTS

Of the models utilized in this study, the RF model yielded the highest AUC of 92%, whereas the NB model presented the lowest AUC of 68%. Also, the RF model boasted the most robust performance in terms of PPV, reaching 82.4%, and NPV, reaching 86.6%. The ANN and the XGBoost model were neck and neck, with the ANN slightly edging out with a higher PPV of 95.9%, while the XGBoost model boasted an impressive NPV of 98.2%. The RF model singled out the following five factors as the most influential in predicting irrational prescriptions: the type of drug, the type of surgery, the number of comorbidities, the date of surgery after hospitalization, as well as the associated hospital and drug costs.

CONCLUSION

The RF model showcased significantly high level of proficiency in predicting irrational prescriptions among orthopedic perioperative patients, outperforming other models by a considerable margin. It effectively enhanced the efficiency of pharmacist interventions, displaying outstanding performance in assisting pharmacists to intervene with irrational prescriptions.

Integrated neural network and evolutionary algorithm approach for liver fibrosis staging: Can artificial intelligence reduce patient costs?

Background and Aim

Staging liver fibrosis is important, and liver biopsy is the gold standard diagnostic tool. We aim to design and evaluate an artificial neural network (ANN) method by taking advantage of the Teaching Learning-Based Optimization (TLBO) algorithm for the prediction of liver fibrosis stage in blood donors and hepatitis C patients.

Methods

We propose a method based on a selection of machine learning classification methods including multilayer perceptron (MLP) neural network, Naive Bayesian (NB), decision tree, and deep learning. Initially, the synthetic minority oversampling technique (SMOTE) is performed to address the imbalance in the dataset. Afterward, the integration of MLP and TLBO is implemented.

Results

We propose a novel algorithm that reduces the number of required patient features to seven inputs. The accuracy of MLP using 12 features is 0.903, while that of the proposed MLP with TLBO is 0.891. Besides, the diagnostic accuracy of all methods, except the model designed with the Bayesian network, increases when the SMOTE balancer is applied.

Conclusion

The decision tree-based deep learning methods show the highest levels of accuracy with 12 features. Interestingly, with the use of TLBO and seven features, MLP reached an accuracy rate of 0.891, which is quite satisfactory when compared with those of similar studies. The proposed model provides high diagnostic accuracy, while reducing the required number of properties from the samples. The results of our study show that the recruited algorithm of our study is more straightforward, with a smaller number of required properties and similar accuracy.

The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine

In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.

Using ChatGPT-4 to Create Structured Medical Notes From Audio Recordings of Physician-Patient Encounters: Comparative Study

BACKGROUND

Medical documentation plays a crucial role in clinical practice, facilitating accurate patient management and communication among health care professionals. However, inaccuracies in medical notes can lead to miscommunication and diagnostic errors. Additionally, the demands of documentation contribute to physician burnout. Although intermediaries like medical scribes and speech recognition software have been used to ease this burden, they have limitations in terms of accuracy and addressing provider-specific metrics. The integration of ambient artificial intelligence (AI)-powered solutions offers a promising way to improve documentation while fitting seamlessly into existing workflows.

OBJECTIVE

This study aims to assess the accuracy and quality of Subjective, Objective, Assessment, and Plan (SOAP) notes generated by ChatGPT-4, an AI model, using established transcripts of History and Physical Examination as the gold standard. We seek to identify potential errors and evaluate the model's performance across different categories.

METHODS

We conducted simulated patient-provider encounters representing various ambulatory specialties and transcribed the audio files. Key reportable elements were identified, and ChatGPT-4 was used to generate SOAP notes based on these transcripts. Three versions of each note were created and compared to the gold standard via chart review; errors generated from the comparison were categorized as omissions, incorrect information, or additions. We compared the accuracy of data elements across versions, transcript length, and data categories. Additionally, we assessed note quality using the Physician Documentation Quality Instrument (PDQI) scoring system.

RESULTS

Although ChatGPT-4 consistently generated SOAP-style notes, there were, on average, 23.6 errors per clinical case, with errors of omission (86%) being the most common, followed by addition errors (10.5%) and inclusion of incorrect facts (3.2%). There was significant variance between replicates of the same case, with only 52.9% of data elements reported correctly across all 3 replicates. The accuracy of data elements varied across cases, with the highest accuracy observed in the "Objective" section. Consequently, the measure of note quality, assessed by PDQI, demonstrated intra- and intercase variance. Finally, the accuracy of ChatGPT-4 was inversely correlated to both the transcript length (P=.05) and the number of scorable data elements (P=.05).

CONCLUSIONS

Our study reveals substantial variability in errors, accuracy, and note quality generated by ChatGPT-4. Errors were not limited to specific sections, and the inconsistency in error types across replicates complicated predictability. Transcript length and data complexity were inversely correlated with note accuracy, raising concerns about the model's effectiveness in handling complex medical cases. The quality and reliability of clinical notes produced by ChatGPT-4 do not meet the standards required for clinical use. Although AI holds promise in health care, caution should be exercised before widespread adoption. Further research is needed to address accuracy, variability, and potential errors. ChatGPT-4, while valuable in various applications, should not be considered a safe alternative to human-generated clinical documentation at this time.

Perceptions of Family Physicians About Applying AI in Primary Health Care: Case Study From a Premier Health Care Organization

BACKGROUND

The COVID-19 pandemic has led to the rapid proliferation of artificial intelligence (AI), which was not previously anticipated; this is an unforeseen development. The use of AI in health care settings is increasing, as it proves to be a promising tool for transforming health care systems, improving operational and business processes, and efficiently simplifying health care tasks for family physicians and health care administrators. Therefore, it is necessary to assess the perspective of family physicians on AI and its impact on their job roles.

OBJECTIVE

This study aims to determine the impact of AI on the management and practices of Qatar's Primary Health Care Corporation (PHCC) in improving health care tasks and service delivery. Furthermore, it seeks to evaluate the impact of AI on family physicians' job roles, including associated risks and ethical ramifications from their perspective.

METHODS

We conducted a cross-sectional survey and sent a web-based questionnaire survey link to 724 practicing family physicians at the PHCC. In total, we received 102 eligible responses.

RESULTS

Of the 102 respondents, 72 (70.6%) were men and 94 (92.2%) were aged between 35 and 54 years. In addition, 58 (56.9%) of the 102 respondents were consultants. The overall awareness of AI was 80 (78.4%) out of 102, with no difference between gender (P=.06) and age groups (P=.12). AI is perceived to play a positive role in improving health care practices at PHCC (P<.001), managing health care tasks (P<.001), and positively impacting health care service delivery (P<.001). Family physicians also perceived that their clinical, administrative, and opportunistic health care management roles were positively influenced by AI (P<.001). Furthermore, perceptions of family physicians indicate that AI improves operational and human resource management (P<.001), does not undermine patient-physician relationships (P<.001), and is not considered superior to human physicians in the clinical judgment process (P<.001). However, its inclusion is believed to decrease patient satisfaction (P<.001). AI decision-making and accountability were recognized as ethical risks, along with data protection and confidentiality. The optimism regarding using AI for future medical decisions was low among family physicians.

CONCLUSIONS

This study indicated a positive perception among family physicians regarding AI integration into primary care settings. AI demonstrates significant potential for enhancing health care task management and overall service delivery at the PHCC. It augments family physicians' roles without replacing them and proves beneficial for operational efficiency, human resource management, and public health during pandemics. While the implementation of AI is anticipated to bring benefits, the careful consideration of ethical, privacy, confidentiality, and patient-centric concerns is essential. These insights provide valuable guidance for the strategic integration of AI into health care systems, with a focus on maintaining high-quality patient care and addressing the multifaceted challenges that arise during this transformative process.